Alexa's Input (AI)

AI agents have created more possibilities for engineers than ever before. But what does daily life actually look like for the builders who've gone all in?

In my very first panel episode, I sit down with Adam Anzuoni from Cursor, Taylor Dolezal from Dosu, and Peter Bell from Gather.dev. Three builders running agents every day for real work, not demos. Peter runs nine API plans across three Mac Minis, with built-in adversarial review....

Most engineers think reliability means avoiding outages. Vlad Leyberov learned the opposite lesson: sometimes you have to intentionally cause a 100% outage to fix the system faster.

Vlad is a Site Reliability Engineer (SRE) at Google, running systems that handle billions of requests per second. Before Google, he kept critical infrastructure running at Meta (billions of events a day) and Amazon (millions of Alexa devices).

In this con...

In this episode of Alexa's Input (AI), I sit down with David Aronchick, co-founder and CEO of Expanso and former product lead for Kubernetes at Google.

Data is growing everywhere outside your data center. Solar panels in remote across a country. Security cameras at retail stores. IoT sensors across factory floors. And moving that data to the cloud for processing? It's expensive, slow, and often restricted by compliance.

David...

In this episode of Alexa’s Input (AI), I sat down with Rob Shaw from Red Hat to talk about how AI inference evolved from a simple model serving problem into a large-scale distributed systems problem.

We explored the infrastructure shifts behind modern LLM serving, including how vLLM and PagedAttention changed the economics and efficiency of inference, why KV cache management became one of the most important bottlenecks in prod...

On this episode of Alexa’s Input (AI), I sit down with Emilio Andere, co-founder and CEO of Wafer, to talk about the future of AI infrastructure, inference optimization, and the economics driving the AI compute race.

We discuss:

• why “intelligence per watt” may become one of the defining metrics of the AI era
• the current GPU and accelerator landscape across NVIDIA, AMD, TPUs, and emerging hardware startups
• why soft...

In this episode of Alexa’s Input (AI), I sit down with Sal Furino to explore the hidden engineering work that keeps modern systems reliable.

We break down what Service Level Objectives, Indicators (SLOs/SLIs), and error budgets actually mean in practice, why reliability is as much a cultural problem as a technical one, and how teams can better measure real user experience instead of just infrastructure health.

Sal also explains...

In this episode of Alexa’s Input (AI), I sit down with Kaan Divitoğlu, founder of Laila — a New York based startup rethinking online dating as a social marketplace centered around real plans instead of endless swiping.

We talk about why traditional dating apps struggle to create real-world connection, how marketplace dynamics shape modern dating behavior, and why Kaan believes the future of dating products is less about ...

In this episode, Alexa Griffith interviews Brady Jordan, a creative director and entrepreneur, who shares his journey from aspiring software engineer to the founder of Clip Play Media and the photo app Y2Cam. Brady discusses the intersection of creativity and technology, the importance of storytelling in video production, and the challenges of self-employment. He emphasizes the need for resilience, adaptability, and a consumer-firs...

Modern software is built on layers and layers of code. So how do we know we can trust it?

In this episode of Alexa’s Input (AI), Alexa Griffith sits down with Justin Cappos, professor of computer science at NYU and a leading expert in software supply chain security, to unpack what trust really means in today’s digital infrastructure.

From package managers and dependency chains to large-scale outages and AI systems built o...

As AI systems grow more autonomous, the question is no longer just what they can do, but whether we can trust the data and models behind their decisions. In this episode of Alexa’s Input (AI), Alexa Griffith talks with Wendy Chin, CEO of PureCipher, about building what she calls an artificial immune system for AI, a framework designed to make data, models, and inference tamper-evident across the AI lifecycle.

They unpack what ...

As LLM apps evolve from simple chatbots to tool-using agents, the attack surface explodes, and the old security playbooks don’t hold. In this episode of Alexa’s Input (AI), Alexa Griffith sits down with Ian Webster, co-founder and CEO of PromptFoo, to break down what AI security actually looks like in practice: automated red teaming, prompt injection and jailbreak testing, evaluation workflows that scale, and why &ldquo...

Most AI infrastructure today is hitting a breaking point. Marc Austin, CEO of Hedgehog, reveals how open source networking and cloud-native solutions are revolutionizing how enterprises build and operate AI at scale. This episode addresses issues many building AI infrastructure today are facing — expensive proprietary systems, overwhelming complex network configurations, and ways to make on-prem AI infrastructure feel just li...

Five years ago, Kelsey Hightower helped me find my voice in tech as the guest for my fifth podcast episode. Today, the man who taught the world Kubernetes and became a legend for his live demos returns for a conversation that goes far beyond infrastructure and code.

Now retired-ish, Kelsey has transitioned into a new chapter. In this episode, we explore what it means to be not only a senior engineer, but also a "senior human" in an ...

In this episode of Alexa’s Input (AI), Alexa sits down with Joe Beda, co-creator of Kubernetes and one of the key figures behind modern cloud computing.

Joe talks through his journey from big tech to founding a startup and back again, and what it actually takes to build systems that scale technically, organizationally, and emotionally. Joe shares the origin story of Kubernetes, what people often misunderstand about open source...

Why do so many AI rollouts stall right after the tools ship?
In this episode of Alexa’s Input (AI), Alexa talks with Melissa Reeve, author of the book Hyper Adaptive: Rewiring the Enterprise to Become AI Native, about what it actually takes to get AI adopted in large organizations.

Melissa shares how her background in lean, Agile, and DevOps transformation shaped her view that AI adoption is less about “buying the too...

What does it actually take to move machine learning from experiments into production reliably, responsibly, and at scale?

In this episode of Alexa’s Input (AI), Alexa talks with Maria Vechtomova, co-founder of Marvelous MLOps and an O’Reilly author-in-progress on MLOps with Databricks. Maria shares how her background in data science led her into MLOps, and why most teams struggle not because of tools, but because of miss...

What if your building was smarter and optimized energy, security, and tenant experience in the background?

In this episode of Alexa’s Input (AI), Alexa talks with Aaron Short, founder and CEO of B-Line, an all-in-one facility management platform that uses AI to automate building operations. Aaron shares how his early work in urban planning and green building led him to build an “operating system for buildings” that...

In this episode of Alexa's Input (AI) Podcast, host Alexa Griffith sits down with Liana Tomescu, founder of Sonny Labs and host of the AI Hacks podcast. Dive into the world of AI security and compliance as Liana shares her journey from Microsoft to founding her own company. Discover the challenges and opportunities in making AI applications secure and compliant, and learn about the latest in AI regulations, including the EU AI Act....

In this episode, Alexa welcomes Swagat Kulkarni, a Senior Solutions Architect at Amazon Web Services, to discuss the intersection of AI, cloud innovation, and enterprise reality. Swagat shares his experience guiding large-scale digital transformations, emphasizing that success often hinges on people and culture, not just technology. They explore the rise of agentic AI, the challenges of moving from proof-of-concept to production , ...